code: def create_model(num_classes):
      import torchvision
      from torchvision.models.feature_extraction import (
          create_feature_extractor,
      )
  
      # vgg16
      backbone = torchvision.models.vgg16_bn(pretrained=True)
      # print(backbone)
      backbone = create_feature_extractor(
          backbone, return_nodes={"features.42": "0"}
      )
      # out = backbone(torch.rand(1, 3, 224, 224))
      # print(out["0"].shape)
      backbone.out_channels = 512
  
      # resnet50 backbone
      # backbone = torchvision.models.resnet50(pretrained=True)
      # # print(backbone)
      # backbone = create_feature_extractor(backbone, return_nodes={"layer3": "0"})
      # # out = backbone(torch.rand(1, 3, 224, 224))
      # # print(out["0"].shape)
      # backbone.out_channels = 1024
  
      # EfficientNetB0
      # backbone = torchvision.models.efficientnet_b0(pretrained=True)
      # # print(backbone)
      # backbone = create_feature_extractor(backbone, return_nodes={"features.5": "0"})
      # # out = backbone(torch.rand(1, 3, 224, 224))
      # # print(out["0"].shape)
      # backbone.out_channels = 112
  
      anchor_generator = AnchorsGenerator(
          sizes=((32, 64, 128, 256, 512),),
          aspect_ratios=((0.5, 1.0, 2.0),),
      )
  
      roi_pooler = torchvision.ops.MultiScaleRoIAlign(
          featmap_names=["0"],  # 在哪些特征层上进行RoIAlign pooling
          output_size=[7, 7],  # RoIAlign pooling输出特征矩阵尺寸
          sampling_ratio=2,
      )  # 采样率
  
      model = FasterRCNN(
          backbone=backbone,
          num_classes=num_classes,
          rpn_anchor_generator=anchor_generator,
          box_roi_pool=roi_pooler,
      )
  
      return model
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/change_backbone_without_fpn.py
imports: import os
  import datetime
  import torch
  import transforms
  from network_files import FasterRCNN, AnchorsGenerator
  from my_dataset import VOCDataSet
  from train_utils import GroupedBatchSampler, create_aspect_ratio_groups
  from train_utils import train_eval_utils
label_desc: Use image node_id 1 for calling a global function with example usage: create_model(num_classes) and returns: model